title: Using NLP to identify cancer cases in imaging reports drawn from radiology information systems.
abstract: A Natural Language processing (NLP) classifier has been developed for the Victorian and NSW Cancer Registries with the purpose of automatically identifying cancer reports from imaging services, transmitting them to the Registries and then extracting pertinent cancer information. Large scale trials conducted on over 40,000 reports show the sensitivity for identifying reportable cancer reports is above 98% with a specificity above 96%. Detection of tumour stream, report purpose, and a variety of extracted content is generally above 90% specificity. The differences between report layout and authoring strategies across imaging services appear to require different classifiers to retain this high level of accuracy. Linkage of the imaging data with existing registry records (hospital and pathology reports) to derive stage and recurrence of cancer has commenced and shown very promising results.
authors.affiliation: Health Language Laboratories Pty Ltd.
authors.forename: Jon
authors.initials: J
authors.lastname: Patrick
authors.forename: Pooyan
authors.initials: P
authors.lastname: Asgari
authors.forename: Min
authors.initials: M
authors.lastname: Li
authors.forename: Dung
authors.initials: D
authors.lastname: Nguyen
mesh_terms: D003952:Diagnostic Imaging; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D009369:Neoplasms / Q000175:diagnosis* / Q000453:epidemiology; D009517:New South Wales / Q000453:epidemiology; D011873:Radiology Information Systems; D012042:Registries; D012680:Sensitivity and Specificity; D014739:Victoria / Q000453:epidemiology